jurnal metabolik

7/28/2019 jurnal Metabolik

http://slidepdf.com/reader/full/jurnal-metabolik 1/26

Why Have Americans Become MoreObese?

David M. Cutler, Edward L. Glaeser and Jesse M. Shapiro

In the early 1960s, the average American adult male weighed 168 pounds.

Today, he weighs nearly 180 pounds. Over the same time period, the average

female adult weight rose from 143 pounds to over 155 pounds (U.S. Depart-

ment of Health and Human Services, 1977, 1996). In the early 1970s, 14 percent of

the population was classified as medically obese. Today, obesity rates are two times

higher (Centers for Disease Control, 2003). Weights have been rising in the United States throughout the twentieth

century, but the rise in obesity since 1980 is fundamentally different from past

changes. For most of the twentieth century, weights were below levels recom-

mended for maximum longevity (Fogel, 1994), and the increase in weight repre-

sented an increase in health, not a decrease. Today, Americans are fatter than

medical science recommends, and weights are still increasing. While many other

countries have experienced significant increases in obesity, no other developed

country is quite as heavy as the United States.

What explains this growth in obesity? Why is obesity higher in the United States

than in any other developed country? The available evidence suggests that caloriesexpended have not changed significantly since 1980, while calories consumed have

risen markedly. But these facts just push the puzzle back a step: why has there been

an increase in calories consumed? We propose a theory based on the division of

labor in food preparation. In the 1960s, the bulk of food preparation was done by

families that cooked their own food and ate it at home. Since then, there has been

a revolution in the mass preparation of food that is roughly comparable to the mass

y David M. Cutler is Professor of Economics, Edward L. Glaeser is Professor of Economics,

and Jesse M. Shapiro is a Ph.D. student in economics, all at Harvard University, Cambridge,Massachusetts. Cutler and Glaeser are also Research Associates, National Bureau of Economic

Research, Cambridge, Massachusetts.

Journal of Economic Perspectives—Volume 17, Number 3—Summer 2003—Pages 93–118



production revolution in manufactured goods that happened a century ago. Tech-

nological innovations—including vacuum packing, improved preservatives, deep

freezing, artificial flavors and microwaves—have enabled food manufacturers to

cook food centrally and ship it to consumers for rapid consumption. In 1965, amarried women who didn’t work spent over two hours per day cooking and

cleaning up from meals. In 1995, the same tasks take less than half the time. The

switch from individual to mass preparation lowered the time price of food con-

sumption and led to increased quantity and variety of foods consumed.

Our theory is nicely illustrated by the potato. Before World War II, Americans

ate massive amounts of potatoes, largely baked, boiled or mashed. They were

generally consumed at home. French fries were rare, both at home and in restau-

rants, because the preparation of French fries requires significant peeling, cutting

and cooking. Without expensive machinery, these activities take a lot of time. In thepostwar period, a number of innovations allowed the centralization of French fry

production. French fries are now typically peeled, cut and cooked in a few central

locations using sophisticated new technologies. They are then frozen at Ϫ40 de-

grees and shipped to the point of consumption, where they are quickly reheated

either in a deep fryer (in a fast food restaurant), in an oven or even a microwave

(at home). Today, the French fry is the dominant form of potato and America ’s

favorite vegetable. This change shows up in consumption data. From 1977 to 1995,

total potato consumption increased by about 30 percent, accounted for almost

exclusively by increased consumption of potato chips and French fries.

The technical change theory has several implications, which we test empiri-cally. First, increased caloric intake is largely a result of consuming more meals

rather than more calories per meal. This is consistent with lower fixed costs of food

preparation. Second, consumption of mass produced food has increased the most

in the past two decades. Third, groups in the population that have had the most

ability to take advantage of the technological changes have had the biggest in-

creases in weight. Married women spent a large amount of time preparing food in

1970, while single men spent little. Obesity increased much more among married

women. Finally, we show that obesity across countries is correlated with access to

new food technologies and to processed food. Food and its delivery systems are

among the most regulated areas of the economy. Some regulations are explicit; forexample, the European Union has taken a strong stance against genetically engi-

neered food, and Germany for many years had a Beer Purity Law. Other “regula-

tions” are cultural, like Jose Bove’s crusade against McDonald’s in France. Coun-

tries with a greater degree of regulation that support traditional agriculture and

delivery systems have lower rates of obesity.

While the medical profession deplores the increase in obesity, the standard

economic view is that lower prices for any good—either monetary or time costs—

expand the budget set and make people better off. But self-control issues compli-

cate this interpretation. If people have dif ficulty controlling how much they eat,lowering the time costs of food consumption may exacerbate these problems.

Certainly, the $40 –$100 billion spent annually on diets testifies to the self-control

94 Journal of Economic Perspectives



problems that many people face. In the last part of the paper, we consider the

welfare implication of lower food production costs when individuals have self-

control problems. We will argue that for the vast majority of people, the price

reductions in food preparation have led to welfare increases.

Trends in Obesity

We make extensive use of the health and weight data from the National Health

and Nutrition Examination Surveys (NHANES) that were conducted in 1959 –1962,

1971–1975, 1976–1980, 1988–1994 and 1999 –2001. We present data through 1999

where we can, but conduct most of our detailed analysis using data through 1994.

The NHANES data measure height and weight directly, using mobile research vans,

so obesity calculations are exact. This method is increasingly important as more

people are overweight and embarrassed to admit it. The available, if somewhat

sporadic, historical data on heights and weights has been compiled by Costa and

Steckel (1997).

The primary measure of obesity is Body Mass Index, or BMI, which allows

comparisons of weight holding height constant. BMI is measured as weight in

kilograms divided by height in meters squared. Optimal BMI levels are generally

believed to lie between 20 and 25. BMI below 20 is considered thin, BMI between

25 and 30 is overweight, and BMI above 30 is obese. A six-foot-tall man wouldtherefore be overweight at 184 pounds and obese at 221 pounds. The medical

evidence shows increasingly high rates of disease and death as BMI increases above

25 (World Health Organization, 2000; Sturm, 2002).1

Early in the twentieth century, Body Mass Index was either optimal medically

or too low, depending on the country (Costa and Steckel, 1997; Fogel, 1994).

Between 1894 and 1961, average BMI for men in their 40s increased from 23.6 to

26.0, with a somewhat smaller, but comparable, increase for men in their 30s. The

increase for men in their 40s corresponds to roughly 16 pounds for a typical

American male (fi ve feet, nine inches tall). Fogel (1994) shows that increases in

BMI over the past few centuries were a major source of improved health. However,since 1960, BMI has increased by another 0.7. The weight increases in the more

recent period are substantially less healthy than in the earlier time period. An

average BMI above 25 places a large share of people in the medically overweight

category. Over the past four decades, the share of the population that is either

overweight or obese increased from 45 to 61 percent. The share of people that are

obese increased from 13 percent to 27 percent. Obesity has increased for both men

and women. For both men and women, most of this increase is in the 1980s and

1990s, and our analysis will focus on this period, as well.

1 But see Campos (2003) for a reevaluation of this evidence.

David M. Cutler, Edward L. Glaeser and Jesse M. Shapiro 95



The Demographics of U.S. Obesity

Not only is average weight increasing, but the right tail of the distribution is

expanding particularly rapidly. Figure 1 shows the distribution of the Body Mass

Index between the 1971–1975 and 1988–1994 surveys. Over this time, median BMI

increased by 0.9; the 75th percentile increased by 1.5; and the 95th percentile

increased by 2.7. While eating disorders, such as anorexia nervosa, are believed tohave increased over the past 30 years (Hsu, 1996), the prevalence of this disease is

still very low. We do not find a significant increase in the population with very low

weight even among younger women. The U.S. Surgeon General estimates that

Figure 1

Distribution of BMI, 1971–1975 and 1988–1994

Males, age 20–55

10

0

.05

.1

20

1971–751988–94

30

BMI (kg/m2)

40 50

Females, age 20–55

10

0

.05

.1

20

1971–751988–94

30BMI (kg/m2)

40 50

Source: National Health and Nutrition Examination Surveys.




around 0.1 percent, or 300,000 people, suffer from anorexia nervosa (U.S. Depart-

ment of Health and Human Services, 1999).

Table 1 shows data on obesity for adults. The left columns report average BMI;

the right columns report the share of the population that is obese. The average

increase in BMI between the 1970s and the 1990s, shown in the first row, is 1.9.

There are some differential increases in obesity by demographic group, which we

examine later in the paper. As a preview of these later arguments, married women

and women with exactly 12 years of schooling have had the largest increases in

average BMI. These groups traditionally spent a lot of time preparing meals at

home, and they spend less time now. However, Table 1 also provides some first

evidence that increased obesity is not a result of women working. Holding constant

obesity within demographic groups, the shift to more households with women

working can account for no more than 10 percent of increased obesity (Cutler,

Glaeser and Shapiro, 2003).2

2 But see Chou, Grossman and Saffer (2002), who argue that increased labor market attachment hasplayed an important role in the rise of obesity in the United States.

Table 1

Increase in Weight by Population Group

Average BMI

(kg/m 2 )

Percentage Obese

(BM Ն 30)

1971– 75 1988 – 94 1971– 75 1988 – 94

Average 25.4 27.3 16% 30% Adults

All 25.0 27.1 15 28Single male 24.4 25.5 9 18Married male, nonworking spouse 25.6 27.1 13 26Married male, working spouse 25.7 27.3 11 24Single female 24.9 27.4 18 32

Married female, working 24.3 27.4 13 33Married female, not working 24.9 28.0 16 36

Elderly All 26.1 27.6 19 32Male 25.4 27.0 13 28Female 26.7 28.2 25 36

Women aged 20ϩ, by education groupϽHigh school 26.3 28.4 24 38High school 24.2 27.5 13 33College or more 22.8 25.4 7 20

Men aged 20ϩ, by education groupϽHigh school 25.6 26.5 15 23High school 25.7 26.7 13 24

College or more 25.2 26.4 8 21

Notes: Data are from the National Health and Nutrition Examination Survey (NHANES). BMI ismeasured in kg/m2.

Why Have Americans Become More Obese? 97



The bottom rows of Table 1 show changes in obesity by education group,

separately for men and women. Obesity for women is strongly negatively associated

with education. This was true in the early 1970s and continues to be true today. But

obesity has increased for all education groups. For men, obesity is relatively inde-

pendent of education and has been for the past few decades. These trends belie an

obvious income-based explanation for increasing obesity. Higher incomes, at least

as reflected in increased education, would actually lower obesity. In Cutler, Glaeser

and Shapiro (2003), we confirm in a regression framework that trends in educa-

tion, age, race, marital status, employment, occupation and the employment status

of the spouse of the head of the household explain at most 10 percent of the

increase in BMI and obesity over this time period. Demographic change is not the

explanation here.

International Evidence on Obesity

Figure 2 puts the U.S. experience in international perspective, showing data on

obesity in OECD countries. The United States is a clear outlier, but other countries

are heavy, as well. Obesity levels in several former Warsaw Pact countries are nearly

as high as they are in the United States. Obesity in England is also extremely high.

France, Italy and Sweden rank much lower in their obesity levels, and the Japanese

are quite thin.

Data on changes in obesity across countries are harder to find. Some countries

have scattered information, discussed in Cutler, Glaeser and Shapiro (2003). The

increase in obesity in the United Kingdom is similar to that of the United States,

although it starts from a lower level. Australia has also seen a rise in obesity,

although not as large. Canada, a country that one might think would parallel the

U.S. experience, had much more modest increases in obesity for men and a

decrease in obesity for women between 1978 and 1988, although obesity has

increased since then (Katzmarzyk, 2002). A good theory of the changes in obesity

should be able to explain why obesity has risen so much in some countries and so

little in others.

Calories In versus Calories Out

Arithmetically, people get heavier if they consume more calories or expend

fewer calories. On average, about 3,500 calories is one pound. There are differ-

ences in metabolisms across human beings, and it is also possible that different

caloric expenditures may have different impacts on the amount of weight gained or

lost. But for a typical person, an increase in calorie consumption of 3,500 calories

or a reduction in caloric expenditure of that amount increases weight by one




pound.3 In this section, we evaluate which of these factors explains changes in

obesity.

We start with some basic energy accounting. People burn calories in three

ways. The first is through basal metabolism—the energy cost associated with keep-ing the body alive and at rest. Basal metabolism represents about 60 percent of

energy utilization for most people. The energy cost of basal metabolism depends on

weight: BM R ϭ ␣ ϩ ␤ Weight . The more a person weighs, the more energy is

required to sustain basic bodily functions. Schofield, Schofield and James (1985)

estimate values of ␣ of 879 for men and 829 for women, and ␤ of 11.6 for men and

8.7 for women (ages 30 –60). Different age groups are associated with different

values of these parameters, and the parameters may also vary somewhat with

conditions. However, the substance of our conclusions is unchanged under

reasonable alternative assumptions. A 70 kilogram (155 pound) man burns onaverage about 1,800 calories before he does any activity. A 60 kilogram woman

(132 pounds) burns about 1,400 calories.

The second source of energy expenditure is that processing food requires

energy. This “thermic effect ” is about 10 percent of total energy expenditures

during a day and comes from the thermic effect of food.

Finally, calories are burned by physical activity. The caloric needs of a given

amount of physical activity is proportional to weight: Energy ϭ ¥a

a ⅐ Weight ⅐

3 There has been controversy in recent years over whether other variables, such as the fat or carbohy-

drate composition of food, may also influence weight patterns (Atkins, 2000). Given the lack of scientificconsensus on the importance of energy composition (Bhargava and Guthrie, 2002; Fumento, 2003), weignore these issues in this paper.

Figure 2

Obesity in International Perspective

15

10

U n i t e

d S t a t e

s

U n i t e

d K i n g d o

m

G e r m

a n y

A u s t r

a l i a

I c e l a n

d

N e w Z e

a l a n d

C a n a

d a

C z e c h R e

p u b l i

c S p

a i n

P o r t u

g a l

P o l a n

d

F i n l a n

d

B e l g i u m

I r e l a n

d I t a

l y

A u s t r

i a

S w e d

e n

N e t h

e r l a n

d s

D e n m

a r k

S w i t z

e r l a n

d

F r a n

c e

N o r w

a y J a p

a n

5

0

20

25

30

% o

b e s e

Source: OECD Health Statistics (2000).




Time a , where a

varies with the activity done, a . The units of a are calories per

kilogram-minute, so a

gives a translation between the weight of the individual and

the duration of activity and the total caloric expenditure associated with the activity.

In this literature, a is typically grouped into categories such as light activity, such

as walking or light housework, moderate activity, such as fast walking and garden-

ing, and heavy activity, like strenuous exercise or farm work (Ainsworth et al.,

1993). Summing across activities, we denote an exercise index E ϭ ¥a

a Time

a ,

reflecting total physical activity in a period of time.

In steady-state, calories in equal calories out. Denoting K as daily calories

consumed, this implies a weight equation of the form

K ϭ ␣ ϩ ␤ ϩ E Weight ϩ .1 K .

Using estimates of ␤ and E from the literature (Schofield, Schofield and James,

1985; Whitney and Cataldo, 1983), this equation can be used to estimate that the

10- to 12-pound increase in median weight we observe in the past two decades

requires a net caloric imbalance of about 100 to 150 calories per day.

These calorie numbers are strikingly small. One hundred and fifty calories per

day is three Oreo cookies or one can of Pepsi. It is about a mile and a half of

walking. Given the small size of this change, it is obviously dif ficult, if not impos-

sible, to determine exactly what explains it. The detailed data on dietary habits andactivities that would be needed to examine this question do not exist. Accordingly,

we use more indirect measures to infer the causes of rising weight. We discuss

evidence on changing intake first and then turn to energy expenditure.

Evidence on Caloric Intake

There are two sources of data on food intake: food recall studies and agricul-

tural sales data. Detailed food recall data are available for 1977–1978 and 1994 –

1996 from the Continuing Surveys of Food Intake by Individuals, conducted by the

U.S. Department of Agriculture.4

In a food recall study, respondents detail every-thing they ate in the previous 24-hour period. In principle, all food consumption

is recorded. In practice, consumption is surely understated, as people do not record

everything they eat. For example, the average male in 1994 –1996 reports consum-

ing 2,347 calories, and the average female reports consuming 1,658 calories. These

imply steady-state weights considerably below those measured for the same popu-

4 In food recall studies, respondents are contacted and asked to recall all food eaten in the previous

two-hour period. Respondents are then asked to keep detailed food diaries for the next one or two days.Consistent with other researchers, we use consumption information from only the first day, althoughthese too are believed to be underreported (Enns, Goldman and Cook, 1997).




lation. Underreporting is not necessarily a problem for our analysis, if the extent of

underreporting is constant over time, but as surveys have improved, underreport-ing has likely fallen.

Table 2 shows changes in food consumption between the mid-1970s and the

mid-1990s for males and females. The top row in each panel reports overall caloric

intake. Reported consumption increased by 268 calories for men and 143 calories

for women between the two surveys. This increase is more than enough to explain

the increase in steady-state weight.

The rows of the table show the distribution of those calories by meal. Some-

what surprisingly, most of the increase in calories is from calories consumed during

snacks. Dinnertime calories have actually fallen somewhat. The increase in caloric

intake is because of greater frequency of eating, not eating more at any one sitting.In calculations not shown in the table, we find that the number of snacks in the

typical day increased dramatically over this period. Whereas only about 28 percent

of people in 1977–1978 reported two or more snacks per day, 45 percent reported

two or more snacks in 1994 –1996. The average number of snacks per day increased

by 60 percent over this period, thus more snacks per day —rather than more

calories per snack—account for the majority of the increase in calories from snacks.

The finding that increased caloric intake is from more snacks rules out two

obvious accounting explanations for increased obesity. The first is that obesity is a

result of increased portion sizes in restaurants (Young and Nestle, 2002). If thistheory were true, calories at main meals, particularly dinner, would have increased.

Similarly, the evidence also rules out the view that fattening meals at fast food

Table 2

Changes in Food Consumption, 1977–1978 to 1994 –1996

Meal

Calories a

Change

Percentage

of Total

Change 1977 – 1978 1994 – 1996

Male TOTAL 2080 2347 268 100%Breakfast 384 420 36 13Lunch 517 567 50 19Dinner 918 859 Ϫ59 Ϫ22Snacks 261 501 241 90Calories per meal 573 566 Ϫ7Meals per day 3.92 4.53 .61

Female TOTAL 1515 1658 143 100%

Breakfast 286 312 26 18Lunch 368 398 31 22Dinner 676 602 Ϫ74 Ϫ52Snacks 186 346 160 112Calories per meal 422 408 Ϫ14Meals per day 3.86 4.44 .58

Note: Data are from the Continuing Survey of Food Intake 1977–1978 and 1994 –1996.a Average calories except for the row reporting average meals per day.




restaurants have made America obese. The food diaries also present evidence on

where calories are consumed. Fast food has certainly increased, from about 60 cal-

ories per day to over 200 calories per day. But this increase is largely at formal

meals, where it has been offset by reduced home consumption. The increase insnacks, in contrast, is largely concentrated in snacks consumed at home and, to a

lesser extent, in snacks purchased in stores and restaurants.

We also examine data on food sales, taken from total production and adjusted

for exports, imports and feed stock (U.S. Department of Agriculture, 2000). In

recent years, the data have also been adjusted for wastage, although this adjustment

is imprecise. Food supply declined relatively steadily between 1909 and 1950. There

were significant downturns during World War I and the Great Depression and

moderate declines in other periods. This decline is almost certainly related to

reduced need for food, as people moved off of farms and into cities. The declinein food consumption helps to explain why obesity increased only mildly during this

earlier time period, despite a large reduction in energy expenditures. Since 1965,

however, food supply has increased markedly, particularly in the last two decades.

In 1978, food supply was 3,200 calories per person. By 1999, food supply was 3,900

calories per person, 700 calories higher. Adjusted for wastage, the increase is

418 calories. This change is three to four times the increase that is needed to

explain the increase in average obesity over the time period.

Evidence on Energy Expenditure

We examine two components of energy expenditure: voluntary exercise andinvoluntary energy expenditure associated with employment. Data on voluntary

exercise come from time diary studies. As with food diaries, the very act of keeping

the diaries induces some people to alter their behavior. Moreover, some of the data

is retrospective, and there are natural memory problems. People may lie, as well.

Still, these problems may not bias trends in time allocation, which is our concern.

Table 3 displays information on time usage in 1965, 1975, 1985 and 1995. Time

use has been remarkably stable over time. The biggest change occurred between

1965 and 1975, when television watching increased by 40 minutes. Some of the

increase in TV time appears to have come out of other forms of socializing

(Putnam, 2000) and a decline in meal cleanup activities. Using our energy expen-diture equation above, we calculate that a 40-minute change from light household

activity to sedentary activity would lead to a four-pound increase in steady-state

weight for the average male.5 However, since 1975, television viewing has increased

by 22 minutes, half of the increase in the previous decade. Furthermore, this rise

in TV viewing has been offset by a decline in other passive categories, such as

sleeping, and an increase in more active categories, such as sports or walking. At the

bottom of Table 3, we calculate values of E —the energy expenditure index—for the

5 Forty minutes of sedentary activity burns about 50 kilocalories for a 70 kg man (Ainsworth et al., 1993).Light household activity uses about 70 –90 kilocalories for the same person (Ainsworth et al., 1993),corresponding to a difference of about 20 – 40 kilocalories, or 2–4 pounds, in steady state.




different time periods. The estimated value of E fell between 1965 and 1975, but

has been quite stable since then. We cannot explain changes in obesity in the past

two decades on that basis.

The second component of energy expenditure is energy spent on the job andcommuting to work. Philipson and Posner (1999) stress this hypothesis in explain-

ing the increase in obesity over time. This view is certainly true over the longer run.

Between 1910 and 1970, the share of people employed in jobs that are highly active

like farm workers and laborers fell from 68 to 49 percent. Since then, the change

has been more modest. Between 1980 and 1990, the share of the population in

highly active occupations declined by a mere 3 percent, from 45 to 42 percent.

Occupation changes are not a major cause of the recent increase in obesity.

Changes in transportation to work are another possible source of reduced

energy expenditure—driving a car instead of walking or using public transporta-tion. Over the longer time period, cars have replaced walking and public transpor-

tation as means of commuting. But this change had largely run its course by 1980.

Table 3

Time Use, 1965–1995

(Minutes per day, age 18 – 64)

Activity 1965 1975 1985 1995

Paid work 290 258 259 266Eating on the job 11 8 8 —Breaks 8 4 3 1

Household work 146 128 124 102Food preparation 44 41 39 27Meal cleanup 21 12 10 4

Child care 37 31 31 18Obtaining goods and services 51 45 53 49Personal needs and care 622 644 634 632

Meals at home 58 54 50 65Meals out 11 19 19 (meals at

home & out)Sleeping/napping 473 496 479 495

Education and training 12 16 18 23Organizational activities 20 24 18 17Entertainment/social 78 65 65 72Recreation 27 37 43 47

Active sports 5 4 10 13Outdoor 1 7 5 6

Walking/hiking/exercise 1 2 4 5Communication 158 191 195 212

TV 89 129 129 151TOTAL 1440 1440 1440 1440Kcal per minute per kilogram 1.69 1.57 1.62 1.53 E for 70 kilogram man 16.4 13.5 14.7 12.6 E for 60 kilogram woman 15.1 12.3 13.5 11.3

Notes: Time use data from Robinson and Godbey (1997) and authors’ calculations from 1995 time diary.Energy expenditure data from authors’ calculations based on Compendium of Physical Activities.




In 1980, 84 percent of people drove to work, 6 percent walked and 6 percent used

public transportation. In 2000, 87 percent drove to work, 3 percent walked and

5 percent used public transportation (U.S. Department of Commerce, 2000).

Changes of this minor magnitude are much too small to explain the trend inobesity: for a 70 kilogram man with a typical commute time of around 22 minutes,

this change would lead to an increase of less than 0.4 pounds in steady state.

A final piece of evidence on energy expenditure comes from examining

population subgroups. Children and the elderly do not work now, and they did not

work in 1980. However, the data show large increases in obesity among children

and adolescents.6 Further, the elderly may be more active now than in 1980, yet

they are also more obese now than in 1980.

In sum, our results suggest that the most plausible explanation for the rise in

obesity involves increased caloric intake, not reduced caloric expenditure. Giventhe limitations of the evidence, we cannot be certain that this completely explains

the rise in obesity. However, we will accept this conclusion and turn next to theories

of why caloric consumption has increased so greatly.

Technology, the Division of Labor and Obesity

Several theories could potentially explain the increase in caloric intake over

the past 25 years. Price and income changes are one explanation. As people get

richer, they will demand more food. But income changes seem unlikely to explainour results. Income and obesity are negatively associated today, at least for women.

Furthermore, for much of the period, real incomes were not increasing greatly at

the bottom of the income distribution, but obesity for those groups still increased.

Relative price declines for food could also explain increased consumption. How-

ever, from 1970 to 1999, the Consumer Price Index for food items increased only

3 percent slower than the CPI for nonfood items.

We also reject a theory of obesity that the increased numbers of women at work

have increased the demand for eating out —and for eating less healthy food. As we

argued above, increased female labor force participation does not appear linked torising obesity. Furthermore, it is not clear that eating out should increase caloric

intake. Restaurants can cook low-calorie food just as easily as high-calorie food.

Indeed, substitution of dinners from home cooked to eaten out seems not to have

increased caloric intake at dinner.

Here, we propose a new theory of increased obesity based on reductions in the

time cost of food, which in turn has allowed more frequent food consumption of

greater variety and, thus, led to higher weights.

6 Anderson, Butcher and Levine (2002) offer evidence that children of working mothers are more likely to be overweight than children of nonworking mothers, although this effect explains only a smallportion of the total increase in child overweight in the last 30 years.




The Rise of Mass Preparation

Traditionally, consumers took raw agricultural products and transformed

them into edible food. This preparation involved significant amounts of time. As

late as the 1960s, a majority of the total costs of food were preparation and cleanuptime. In 1965, the average family spent $15 per day on purchased food (in 1990

dollars) and about 130 minutes on preparation and cleanup (Robinson and

Godbey, 1997). At an average wage for women, this time cost perhaps $20, or

57 percent of total food expenditures. Over the past 30 years, the time involved in

preparing food has fallen in half.

People could always make almost any form of food that is currently available,

if they were willing to spend the time to do so. For example, ambitious cooks could

make snack-size cream-filled cakes, for example, but it took time. Technological

innovations since 1970 mean that preparation can now be done in restaurants andfactories, exploiting technology and returns to scale. Snack-size cream-filled cakes

are now widely available for less than a dollar.

To produce food in one location that will be nearly ready for consumption in

another location, one must surmount fi ve main technological obstacles (Kelsey,

1989): controlling the atmosphere; preventing spoilage due to microorganisms;

preserving flavor; preserving moisture; and controlling temperature. Innovations in

food processing and packaging over the last three decades have improved food

manufacturers’ ability to address each of these issues.

“Controlled atmosphere processing” and, more recently, “modified atmo-

sphere processing,” allow food manufacturers to control the gaseous environment in which their foods are stored. In the case of fruits, vegetables and other foods with

living cells, these technologies slow down ripening and prevent spoilage. For

recently introduced packaged goods such as fresh pasta, prepared salads and

cooked chicken, control of the atmosphere inside the package can greatly lengthen

shelf life (Testin, 1995).

Hydrogen-peroxide sterilization (approved for use in 1981) and stretch-wrap

films (introduced in 1976) have improved food producers’ ability to kill and seal

out harmful microorganisms. Since the 1970s, food irradiation has made significant

advances, although the diffusion of this technology has been slowed by public

concern and the Food and Drug Administration. A persistent problem in food processing is that packaging can adversely affect

food flavor. The 1980s saw huge advances in “flavor barrier” technology, which

involves materials specially tailored to the food that prevent migration of flavor-

related chemicals to and from the food. In addition, the food industry has increas-

ingly made use of chemists as flavor specialists to design food flavors to suit

consumers’ tastes (Schlosser, 2002). These chemists hone in on what makes certain

foods desirable and synthesize it in the laboratory. These artificial flavors can then

be added to make pre-prepared food more appealing.

Temperature and moisture pose a particular problem in the case of frozenfoods. If moisture builds up in the package, ice crystals can form, which separate

ingredients and alter the food’s texture (Kelsey, 1989). In addition, moisture can




lead to dehydration of food in the freezer and “freezer burn.” Advances in poly-

ethylene plastics and other materials have improved control over the internal

moisture of food packages, thus extending the freezer/shelf life of many foods and

improving flavor.Other technologies are available at the user end. Microwaves were developed

in the 1940s as an outgrowth of radar technology and became widely available in

the 1970s. As late as 1978, only 8 percent of American households had microwaves.

By 1999, 83 percent of American households had microwave ovens (Energy Infor-

mation Administration, 2003, provides more details). Other kitchen appliances,

such as refrigerators, have also improved.

These technologies did not affect all foods or all places equally. Generally,

foods that are consumed in more or less the same form that they leave the farm, like

fresh fruit, stand to gain less from advances in packaging and processing. Foods that involve significant amounts of preparation benefit most from the new technologies.

American technological leadership and the large size of the American market

meant that many of the most important innovations were first developed in the

United States. Other countries have often limited the incursions of American food

products or food retailers (such as fast food outlets). Moreover, food is often one

of the most regulated areas of any the economy, and many economies have put

substantial roadblocks to the incorporation of new food technologies.

Perhaps the most telling evidence for the revolution in time costs of food

production has been the reduction in the time spent cooking and cleaning. Table 4

shows food preparation times for different subgroups of the population in 1965and 1995. The food preparation and clean-up times for both working and non-

working women fell by about 50 percent. These changes hold work status constant.

They reflect technology, not labor force participation.

The trend toward increased levels of commercial preparation also appears in

data on the distribution of food payments. In 1972, 44 percent of the cost of food

went to farmers. By 1997, only 23 percent of the cost of food represented the input

of farmers. The rest is input from the retail sector. This statement does not just

apply to the restaurant sector. Eighty percent of the cost of food eaten at home is

now spent on nonfarm related expenses. Labor in the supermarket and the factory

has replaced labor in the home, and this shift has been associated with dramatictime savings within the home.

Implications of Technological Change

Food preparation involves both fixed and variable costs. For example, peeling

and cutting French fries is a marginal time cost, while deep frying is generally a

fixed cost (up to the point where the fryer is full). Mass preparation means that the

fixed time component can be shared over a wide range of consumers. In addition,

mass preparation reduces the marginal cost of preparing food by substituting

capital for labor. Finally, mass preparation exploits the division of labor. Foodprofessionals instead of everyday people now prepare food, reducing both fixed

and marginal costs.




Reductions in the time cost of food preparation should lead to an increase in

the amount of food consumed, just as reductions in any good’s price should lead

to increased consumption of that good. Based on a standard quantity-quality model

of food consumption, as in Becker and Lewis (1973), this increase can occurthrough several channels: 1) increased variety of foods consumed; 2) increased

frequency of food consumption; 3) a switch to high-calorie/high-flavor prepared

foods that had previously been unavailable; or 4) an increase in the overall

consumption of each individual food item. As fixed costs decline, we would expect

most of the increase in calories to come from increased variety of foods and

frequency of food consumption, rather than more food during each meal. Indeed,

reductions in time costs have an ambiguous effect on calories per food item. If the

quantity of meals and food at each meal are substitutes (for example, as people

become sated), the calories at any given meal will decline.

Testing the Implications of the Theory

The mass preparation theory suggests four empirical implications. First, the

lower costs of food preparation mean that individuals should consume a wider

range of products at more times during the day. Second, the increase in food

consumption should come mostly in foods that had an improvement in mass

preparation technology (and complements to those foods). Third, individuals whohave taken the most advantage of the new technologies should have had the biggest

increase in obesity. Finally, obesity rates should be higher in countries with greater

Table 4

Time Costs by Demographic Group

(minutes)

1965 1995

Meal

Prep.

Meal Prep.

ϩ Cleanup

Meal

Prep.

Meal Prep.

ϩ Cleanup

AdultsSingle male 13.6 18.1 15.5 17.3Married male, nonworking spouse 6.5 9.4 13.2 14.4Married male, working spouse 8.1 11.9 13.2 14.4Single female 38.1 60.1 28.9 33.1Married female, working 58.3 84.8 35.7 41.4

Married female, not working 94.2 137.7 57.7 68.8Elderly

Male 16.6 26.3 18.5 20.2Female 65.9 10.4 50.1 60.3

Source: Authors’ calculations from Americans’ Use of Time Survey Archives, 1965 and 1995.




access to technological changes in food consumption. In this section, we test these

implications.

Implication 1: Changes in Food Type, Composition and Timing

A reduction in the time costs of food preparation should cause people to

consume a greater variety of foods now than in the past and at more times during

the day. We already noted the evidence for this point in Table 2. Snacks are where

a significant portion of the changes in food production have occurred. Snacks are

also largely pre-prepared.

Implication 2: Calories from Different Food Products

Consumption should have increased most for food items that have experi-enced the most time-saving technological change. The best measure of the degree

of mass preparation is the U.S. Department of Agriculture’s measure of the share

of costs going to farmers instead of other food preparers, called the “farm value

share.” Food items with a great deal of mass preparation have low farm values. The

USDA has calculated the farm value share for some food categories: it varies greatly,

from over 60 percent for eggs to near 10 percent for grains. Figure 3 shows the

relationship between farm value share and caloric growth across 13 food categories.

There is a relatively large and statistically significant negative correlation of Ϫ.68

between the two: food items with large amounts of commercial preparation haveincreased in consumption, and food items with less commercial preparation have

fallen.

Figure 3

Food Preparation and Changes in Intake

0

Ϫ.4

.2

0

Ϫ.2

.4

20

meat

potato

veggiegreensmisc

noncitrusgrains

fatssugars

legumescitrus

dairy

eggs

6040Farm Share of Value, 1990

P e r c e n t C h a n g e i n C a l o r i e s ,

1 9 7 0 –

1 9 9 9

80

Notes: Data on calories for each food group are from the Per Capita Food Consumption Data System(2002). Data on farm share of value were obtained by personal correspondence with Howard Elitzakof the United States Department of Agriculture, Economic Research Service. The regression equation is⌬ ln(cals, 1970–1999) ϭ 0.185 (.075) Ϫ 0.008 (.003) farm share of value, 1990; N ϭ 13, Adj.R 2 ϭ 0.409




Implication 3: Changes in Obesity Across Demographic Groups

Obesity should increase the most among groups for whom the costs of food

production fell the most. Thus, the theory predicts that obesity should increase the

most among groups who formerly made most of their food in the house and should

have increased the least among groups that already ate out more.To test this prediction, we relate changes in obesity across demographic groups

to the amount of time spent preparing food in 1965 and to changes in the amount

of time spent preparing food between 1965 and 1995. We divide the adult popu-

lation into the eight demographic groups shown earlier in Table 4. An important

issue is whether the time costs should be for the person or the family. Under the

assumption of joint household decision making, it is the total time usage that

matters, not the individual time spent. In other models, the time that each person

spends in food preparation would matter. For example, if men eat at work in ways

their wives cannot control, we would not expect reduced time costs for wives to have

much effect on weight of married men.Table 5 shows the relationship between the initial time spent preparing food

and the change in BMI. In each column, we regress the change in BMI for each of

the groups in Table 4 on the level or change in average time use for that group

from 1965 to 1995. Column 1 shows the relationship where time spent in food

preparation is calculated for the average individual in the group. In column 2, the

time is calculated for the average household of individuals in the group. There is

a positive relationship between time costs and obesity changes in column 1, but less

so in column 2. Women spend less time preparing food now than they used to, and

they are much more obese than they used to be. The difference between men and women may be related to the fact that variety has increased the most for women

(men already ate out more) or to lack of joint decision making. The results in

Table 5

Time Costs and Changes in BMI

(Dependent variable: change in BMI, 1971– 1975 to 1988 – 1994)

Independent Variable (1) (2) (3)

Sex-specific time cost (min.), 1965 0.0155(0.0027)

Household-specific time cost (min.), 1965 0.0078(0.0055)

Change in sex-specific time cost, 1965–95 Ϫ0.0182(0.0050)

Constant 1.3043 1.3774 1.7983(0.1712) (0.5134) (0.1768)

Observations 8 8 8 Adjusted R -squared 0.8156 0.1223 0.6336

Notes: Standard errors are in parentheses. Data on the change in BMI are from the NHANES surveys of 1971–1975 to 1988 –1994. The initial time cost is from 1965, computed as minutes spent preparing andcleaning up after meals. The data are from the Americans’ Use of Time Survey Archive.




column 1 indicate that each 30 minutes of initial food preparation time is associ-

ated with an increase in BMI of nearly 0.5. This factor does not explain all of the

increase in obesity —the constant is statistically significantly positive— but it ex-

plains a good share. Column 3 shows the relation between the change in BMI andthe change in the time spent preparing food, using person-specific time costs. The

results are similar: groups that saw a large reduction in the time spent preparing

food also had large increases in BMI.

Implication 4: Obesity Across Countries

Reduced time for preparing food should have a greater effect in countries

where the appropriate technological innovations are encouraged. Many countries

have explicit or implicit restrictions on the ability of food producers or consumers

to have access to such technologies. We examine whether such restrictions arerelated to obesity.

Evidence on household appliances certainly suggests that countries differ in

their access to food preparation technology. While over 80 percent of U.S. house-

holds have microwave ovens, in Italy, where obesity is much less common than in

the United States, only 14 percent of households have microwaves (Alberta Agri-

culture, Food, and Rural Development, 2003). By contrast, in the United Kingdom,

which has obesity rates much closer to those of the United States, 66 percent of

households have microwaves. Calculations of the time spent cooking in a typical

day, based on the Multinational Time Use Study (2003) line up similarly. Control-

ling for basic demographics, Italian and French adults spend about 19 moreminutes per day cooking than Americans, whereas those in the United Kingdom

spend almost exactly the same amount of time as Americans.

Our sample, which is determined by the availability of data, is OECD coun-

tries.7 Table 6 shows the results. In all of our regressions, we control for female

labor force participation rates and GDP per capita, to test these theories of obesity.

The first column includes just female labor force participation rates and income.

Neither is significantly related to obesity, nor are they related when other variables

are included. Ideally, we would have data directly on food industry regulation, but

because such data are not always available, we use a number of proxies.

The second column includes the frequency of price controls in the economy as a whole. This variable is an average of the 1989 and 1994 Economic Freedom of the

World index of price controls (Gwartney, Lawson and Block, 1995). The index

ranges from 0 to 10. We have normalized it to have a mean of 0 and a standard

deviation of 1. People in OECD countries with more price controls are much less

obese than people in countries without price controls. A one standard deviation

increase in price controls is associated with about 3.7 percentage points less obesity.

The third column looks at the relation between producer protection—mea-

sured as the ratio of agricultural prices in the country to and worldwide prices—and

7 The appendix of our working paper, Cutler, Glaeser and Shapiro (2003), shows the countries includedin each regression.




obesity. This variable comes from the OECD’s Producer and Consumer Support Esti-

mates 1999 database. The measure captures tariff and nontariff barriers to agricul-

ture, but is only available for nine countries. A one standard deviation increase in

domestic prices above world prices reduces obesity by a statistically significant

4.5 percentage points. One possible concern is that this relationship is driven by pure

price effects—higher food prices from protectionism would lead to lower consump-

tion—but the strong correlation between our measure of protectionism and our othermeasures of regulation suggests that price effects are not the whole story.

The fourth column includes a count of the number of food laws listed in nine

countries from Kellan and Guanino (2000). These laws include packaging and

labeling requirements, preservative tolerances and pesticide regulations. The mean

country for which data are available has 26 food laws. Although we have only nine

observations, the few observations do suggest that countries with more food laws

have lower levels of obesity.

Recent research has highlighted the link between regulation and the structure

of the legal system (La Porta et al., 1999). Countries with a common law legal origin(the British model) are much less regulated than are countries with a civil law

origin (the French model). The fifth column includes a measure of civil law legal

Table 6

International Regressions

(Dependent variable: percentage of adult population that is obese)

Independent Variable (1) (2) (3) (4) (5) (6) (7)

Frequency of price controlsa Ϫ3.7(1.3)

Producer protectiona Ϫ4.5(1.7)

Number of food statutesa Ϫ7.4(2.2)

Civil law origin Ϫ7.5(2.2)

log(time to open business) Ϫ2.6

(1.1)Cost of a Big Mac (US2000$)a Ϫ4.7

(2.3)log(GDP per capita), 1998 0.68 Ϫ4.63 6.78 5.10 Ϫ1.58 Ϫ4.72 10.65

(4.57) (4.25) (4.59) (3.76) (3.72) (4.56) (6.80)% females in labor force, 1992 0.24 0.04 0.81 0.69 0.26 Ϫ0.15 0.46

(0.31) (0.27) (0.66) (0.41) (0.25) (0.31) (0.47)Constant Ϫ0.35 22.73 Ϫ42.96 Ϫ31.42 11.15 39.75 Ϫ39.30

(19.27) (17.98) (33.38) (22.05) (15.82) (23.15) (31.83)

Observations 22 21 9 9 22 21 13 Adjusted R -squared Ϫ0.072 0.204 0.491 0.310 0.557 0.128 0.124

Notes: Standard errors are in parentheses. Appendix Table 3 shows the available countries and source of data.aData are standardized to have a mean of 0 and standard deviation of 1.




origin to capture the overall prevalence of regulation. More regulated countries are

7 percent less obese than are less regulated countries.

One way that regulation works is to stop new technology. To measure the ease

of technology importation, the sixth column relates obesity to the Djankov et al.(2002) measure of the time required in days to open a new business (in days).

Countries with greater time delays to opening new businesses are less obese than

countries with shorter times.

The last column relates obesity to the price of a Big Mac, taken from the

Economist . Big Mac prices are an approximate measure of relative food costs in

different countries. Countries in which Big Macs cost more are less obese than

countries in which they cost less. Although Big Mac prices will presumably depend

on demand as well as supply, if demand differences were the sole force behind the

price heterogeneity, we would expect to see the opposite pattern: countries withhigher prices having more obesity (assuming that supply is not perfectly elastic).

The results in Table 6 are not definitive, but they certainly support the theory.

People in more regulated countries, and particularly countries with a more regu-

lated agricultural sector, are less obese. Female labor force participation rates and

real income are unrelated to obesity.

Obesity, Self-Control and Consumer Welfare

Lower time costs of food preparation may affect consumption through twochannels. The first is a standard price mechanism. The cost of food consumption

includes time and money costs. As time costs fall, one would expect a standard

demand response to price. This effect could be large enough to explain the

increase in consumption we observe. Reductions in the time required to prepare

food reduced the per calorie cost of food by 29 percent from 1965 to 1995. If the

elasticity of caloric intake with respect to price is Ϫ0.7, this could explain the

increase in caloric intake. An elasticity of Ϫ0.7 is possible, but probably on the high

side. Typical price elasticities for total food consumption are on the order of Ϫ0.6

(Blundell, Browning and Meghir, 1994). The elasticity of caloric intake with respect

to price is likely smaller than this, however, since the food spending elasticity includes increased quality of food in addition to quantity. We do not know how

much smaller, however.

We suspect, however, that this is not the only reason why lower time costs lead

to increased consumption. A second issue is that self-control issues are likely to be

important, as well. The standard model of consumption involves rational individ-

uals who decide how much to consume on the basis of price and income, fully

accounting for the future health consequences of their actions. But at least some

food consumption is almost certainly not fully rational. People overeat, despite

substantial evidence that they want to lose weight. The diet industry has $40 –$100billion in annual revenues (Cummings, 2003). Food brings immediate gratification,

while health costs of overconsumption occur only in the future. Maintaining a diet




can be very dif ficult. People on diets frequently yo-yo; their weight rises and falls as

they start and stop dieting. Survey evidence on the relationship between actual and

self-described optimal weights (from the Behavioral Risk Factor Surveillance Sur-

vey, 2003) confirms this dif ficulty. In general, desired weight rises only slightly withactual weight, particularly for obese individuals.

As a result, people with self-control problems may find themselves overcon-

suming food, particularly when the time costs of food preparation fall. In this

situation, lower time costs of food preparation may be a welfare loss.8 In this

section, we present a framework for self-control problems and evaluate the welfare

implications of technical change in such a situation.

A Model of Self-Control Problems

Consider an individual who discounts all times in the future at a rate higherthan the pure time discount rate, but trades off consumption in future states at the

time discount rate. Such an individual will always want to begin a diet tomorrow,

because the long-term benefits justify the lost utility tomorrow, but not today,

because the immediate gratification from food is high. Reductions in the time cost

of food preparation may reduce the welfare of this person, by increasing the

immediate consumption value of food relative to the long-term health costs.

This model produces the following condition for optimal food consumption:

Discount Factor ϫMarginal Benefit

of Foodϭ

Marginal Time and

Cash Cost of Food.

The marginal benefit of food is discounted because there is a time delay between

the time at which people decide they want to consume and the time at which they

actually do consume. In standard exponential discounting models, such time delays

of one hour or less are far too small to matter. But for hyperbolic individuals, even

these short time delays may matter. Technological change that shortens this time

delay will make eating much more attractive for hyperbolic discounters. As a result,

a hyperbolic discounter will overconsume relative to the consumer’s long-run

interests. In the past, time delays due to food preparation limited the tendency to

overeat. After all, hyperbolic consumers had to wait an hour before satisfying theirdesires. Today, mass preparation means that individuals can satiate their desires

immediately, and as a result, the impatient eat more.

The intuition behind this argument can be illustrated by thinking about a

hungry worker and a vending machine filled with cookies. If the vending machine

is 10 feet away, a person might eat mid-afternoon cookies, even if the worker is on

a diet (the diet can always start tomorrow). The same person, however, might not

be willing to walk 10 minutes to and from the store to get cookies or to spend a

8 Increased food consumption might be a welfare loss for another reason as well—the external costs of individual weight for medical and disability programs. As with smoking, however, we suspect that suchexternal costs are relatively small compared to the internal costs (Gruber and Koszegi, 2001).




half-hour baking cookies (if at home). The benefits of eating cookies that are

10 minutes or one-half hour down the road are too far away. Many behavioral

change programs—like those involved with smoking and drinking cessation as well

as weight loss— encourage keeping the offending items as far away as possible.Raising time costs is believed to reduce consumption.9

In this kind of model, food consumption carries two costs: the value of

foregone consumption of nonfood items and the health and social costs of in-

creased weight. In equilibrium, the consumer will choose caloric intake so that the

marginal benefit of additional food consumption is equal to the costs of foregone

consumption and lower health. Technological innovation that allows mass prepa-

ration of food will impact consumption in two ways: first, through a decline in price

of food (where price is understood as including preparation and clean-up time),

and second, in reducing the delay before consumption. The price reduction willaffect all; the reduced time delay is likely particularly to affect people with self-

control problems. The essence of self-control problems is that people have dif fi-

culty passing up current pleasure for future benefits. Anything that decreases the

delay of benefits exacerbates this problem.

This result helps to explain one of the most striking facts about the recent rise

in obesity —the dramatic increase at the upper tail of the weight distribution.

People with self-control problems are more likely to have high initial weight levels

and are more likely to gain more weight with further improvements in food

technology. This result also helps to explain why reductions in the time cost of food

might have a much larger impact on the level of obesity than reductions in themonetary cost of food. Because reduced time costs affect both the price of the food

and the delay before consumption, hyperbolic consumers will be very sensitive to

changes in time delay, even if they are not very price sensitive.

Welfare Implications of Lower Time Costs

Changes in the time costs of food preparation have two opposing effects on

welfare. The direct impact is that reduced time costs lower total prices of food

consumption and thus raise consumption. This reduces other consumption and

may harm health, but a rational consumer takes these into account. For a rational

consumer, when prices fall, welfare increases. A consumer with self-control prob-lems may well spend more than is optimal on food, however. There are two possible

welfare costs from this behavior. The first cost is the reduction in consumption of

other goods beyond what a rational person would do. We suspect that this term is

small; after all, the chief harm from people overconsuming food is not that they are

immiserized by additional food spending, but the health costs of increased weight.

That health cost of overconsuming is the product of the weight gained and the

9 This situation can be modeled formally using the hyperbolic discounting framework of Laibson (1997)

and Harris and Laibson (2001). We sketch the analysis here and refer readers interested in a formaltreatment to our working paper, Cutler, Glaeser and Shapiro (2003). O’Donohue and Rabin (1999)discuss the effects of immediate versus delayed rewards in a hyperbolic discounting model.




health costs of additional weight, weighted by the degree of nonrational discount-

ing. People are worse off if this health cost is greater than the welfare gain from

lower costs of food preparation. Thus, people are worse off if

Difference between

Standard and

Hyperbolic

Discount Rates

ϫChange

in Weight ϫ

Cost of

Weight Ͼ

Change in

Costs of Food.

If preferences were rational, the difference between standard and hyperbolic

discounting would be zero, and the left-hand side of this equation would be zero.

The health effect would be fully internalized, and welfare would necessarily in-

crease. With nonrational discounting, weight may increase too much, and people

may be worse off.

To compare these terms, we need to express everything in the same units. It is

easiest to evaluate them in units of time. We do not know the monetary willingness

to pay for lower weight, but we can use exercise technology to figure out a rough

estimate of the time cost. In time units, people are worse off if

Difference between

Standard and

Hyperbolic

Discount Rates

ϫTime Costs of Losing

the Weight GainedϾ

Reduction in Time

Costs of Food.

On average in the United States, there has been a reduction in time costs of food

preparation of about 20 minutes per person per day from 1965 to 1995. The

10-pound weight gain over a similar period that corresponds to this time reduction

represents about 100 calories per day, or about one mile of daily exercise. If it takes

15 minutes to walk or jog a mile, the time cost of the 10-pounds gained is about

15 minutes per day.10 The typical person must be better off from the reduction in

time costs. Of the 20 minutes saved in food preparation, people could spend

15 minutes exercising, lose the weight gained, and still have fi ve minutes left over.

The only way people might be made worse off by the reduced time of foodpreparation is if they are particularly impatient and as a result would be willing to

forgo more than 15 minutes per day to lose 10 pounds in steady state, but cannot

seem to do so. A person who always vows to exercise but never starts can be viewed

conceptually as someone willing to pay more than 15 minutes per day to lose

10 pounds.

There is likely to be a difference across people: extremely hyperbolic individ-

uals may be hurt by the change in technology, but people without extreme

self-control problems will be better off. While there is no evidence on the incidence

10 Standard economic logic (setting aside hyperbolic discounting) suggests that the people who don’t exercise probably value losing weight by less than this 15 minutes a day.




of extreme hyperbolic discounting in the population, we suspect that most people

are better off from the technological advances of mass food preparation, even if

their weight has increased.

y We are grateful to Melissa Eccleston, Daniel Michalow and Vladimir Novakovski for

research assistance and to the National Institutes on Aging for research support. We thank

Gary Becker, Darius Lakdawalla, Tomas Philipson and seminar participants at the Ohio

State University and the University of Chicago for helpful comments. Additional data and

models for the points we make in this paper can be found in our working paper, Cutler, Glaeser

and Shapiro (2003).

References

Ainsworth, B. E. et al. 1993. “Compendium of

Physical Activities: Classification of Energy Costs

of Human Physical Activities.” Medicine & Science

in Sports & Exercise. 25:1, pp. 71– 80.

Alberta Agriculture, Food, and Rural Develop-

ment. 2003. Market Reports. Available at

http://www.agric.gov.ab.ca/ .

Anderson, Patricia M., Kristin F. Butcher and

Phillip B. Levine. 2002. “Maternal Employment

and Overweight Children.” NBER Working Pa-per No. 8770.

Atkins, Robert C. 2000. Dr. Atkins ’ New Diet

Revolution . New York: Avon Books.

Becker, Gary S. 1965. “ A Theory of the Allo-cation of Time.” Economic Journal. 40:299, pp.

493–508.

Becker, Gary S. and H. Gregg Lewis. 1973.

“On the Interaction between the Quantity and

Quality of Children.” Journal of Political Economy.

81:2, pp. S279 – 88.

Behavioral Risk Factor Surveillance Survey.

2003. Available at http://www.cdc.gov/brfss/ .

Bhargava, Alok and Joanne F. Guthrie. 2002.

“Unhealthy Eating Habits, Physical Exercise and

Macronutrient Intakes are Predictors of Anthro-

pometric Indicators in the Women’s Health

Trial: Feasibility Study in Minority Populations.”

British Journal of Nutrition. 88:6, pp. 719 –28.

Blundell, Richard, Martin Browning and Cos-

tas Meghir. 1994. “Consumer Demand and the

Life-Cycle Allocation of Household Expendi-tures.” Review of Economic Studies . 61:1, pp. 57–80.

Campos, Paul. 2003. “ Weighting Game: What

the Diet Industry Won’t Tell You.” New Republic .

January 13, p. 17.

Centers for Disease Control. 2001. Health,

United States . Atlanta: Centers for Disease Con-

trol and Prevention.

Centers for Disease Control. 2003. National

Health and Nutrition Examination Survey . Available

at http://www.cdc.gov/nchs/nhanes.htm .

Chou, Shin-Yi, Michael Grossman and Henry

Saffer. 2002. “ An Economic Analysis of Adult

Obesity: Results from the Behavioral Risk Factor

Surveillance System.” NBER Working Paper No.

9247, October.

Costa, Dora and Richard Steckel. 1997. “Long-

Term Trends in Health, Welfare, and Economic

Growth in the United States,” in Health and Wel-

fare during Industrialization . R. Floud and R.

Steckel, eds. Chicago: University of Chicago

Press for National Bureau of Economic Re-search, pp. 47– 89.

Cummings, Laura. 2003. “The Diet Business:

Banking on Failure.” BBC Online . February 5.

Cutler, David M., Edward L. Glaeser and Jesse

M. Shapiro. 2003. “ Why Have Americans Be-

come More Obese?” Harvard Institute for

Economic Research Working Paper No. 1994,

January.

Djankov, Simeon et al. 2002. “The Regulation

of Entry.” Quarterly Journal of Economics. 117:1,

pp. 1–38.

Energy Information Administration. 2003.“Households with Selected Appliances and

Types of Main Heating Fuel, Selected Years,




1978 –2001.” Available at http://www.eia.doe.gov/emeu/aer/txt/ptb0207.html .

Enns, Cecilia Wilkinson, Joseph D. Goldman

and Annetta Cook. 1997. “Trends in Food andNutrient Intakes by Adults: NFCS 1977–78, CSFI1989 –91, and CSFII 1994 –95.” Family Economics

and Nutrition Review. 10:4, pp. 2–15.Fogel, Robert W. 1994. “Economic Growth,

Population Theory, and Physiology: The Bearingof Long-Term Processes on the Making of Eco-nomic Policy.” American Economic Review. 84:3,pp. 369 –95.

Frederick, Shane, George Lowenstein and

Ted O’Donoghue. 2002. “Time Discounting andTime Preference: A Critical Review.” Journal of

Economic Literature. 40:6, pp. 351–401.

Fumento, Michael. 2003. “Big Fat Fake: The Atkins Diet Controversy and the Sorry State of Scientific Journalism.” Reason . March. Availableat http://reason.com/0303/fe.mf.big.shtml .

Greenwood, Jeremy, Ananth Seshadri and Me-hmet Yorukoglu. 2001. “Engines of Liberation.”Mimeo, University of Rochester.

Gruber, Jonathan and Botond Koszegi. 2001.“Is Addiction ‘Rational’? Theory and Evidence.”Quarterly Journal of Economics . November, 116:4,pp. 1261–304.

Gwartney, James, Robert Lawson and Walter

Block. 1995. Economic Freedom of the World 1975 –

1995 . Canada: Fraser Institute.Harris, Christopher and David Laibson. 2001.

“Dynamic Choices of Hyperbolic Discounters.” Econometrica. 69:4, pp. 935–57.

Hsu, L. K. George. 1996. “Epidemiology of the Eating Disorders.” Psychiatric Clinics of North

America. 19:4, pp. 681–700.Katzmarzyk, Peter T. 2002. “The Canadian

Obesity Epidemic.” Canadian Medical Association

Journal . April 16, 166:8, pp. 1039 –2040.Kellam, Jocelyn and Elizabeth T. Guarino.

2000. International Food Laws . Norwich: Statio-

nery Of fice.Kelsey, Robert J. 1989. Packaging in Today ’ s

Society, Third Edition. Lancaster, Pa.: TechnomicPublishing Company, Inc.

Laibson, David. 1997. “Golden Eggs and Hy-perbolic Discounting.” Quarterly Journal of Eco-

nomics. 112:2, pp. 443–77.LaPorta, Rafael et al. 1999. “The Quality of

Government.” Journal of Law, Economics and Or-

ganization. April, 15, pp. 222–79.Manson, JoAnn E. et al. 1995. “Body Weight

and Mortality among Women.” New England Jour-

nal of Medicine. 333:11, pp. 677– 85.

Multinational Time Use Study. 2003. Availableat http://www.iser.essex.ac.uk/mtus/index.php .

O’Donohue, Ted and Matthew Rabin. 1999.

“Doing it Now or Later.” American Economic Re-

view. 89:1, pp. 103–24.

Philipson, Tomas J. and Richard A. Posner.

1999. “The Long-Run Growth in Obesity as a

Function of Technological Change.” NBER

Working Paper No. 7423.

Putnam, Robert. 2000. Bowling Alone: The Col-

lapse and Revival of American Community . New

York: Simon & Schuster.

Quesenberry, Charles P., Bette Caan and Al-

ice Jacobson. 1998. “Obesity, Health Services

Use, and Health Care Costs among Members of

a Health Maintenance Organization.” Archives of

Internal Medicine. 158:5, pp. 466 –72.

Robinson, John P. and Geoffrey Godbey.

1997. Time for Life: The Surprising Ways Americans

Use Their Time . University Park, Pa.: Pennsylvania

State University Press.

Schlosser, Eric. 2002. Fast Food Nation: The

Dark Side of the All-American Meal . New York:

Perennial.

Schofield, W. N., C. Schofield and W. P. T.

James. 1985. “Basal Metabolic Rate—Review and

Prediction Together with Annotated Bibliogra-

phy of Source Material.” Human Nutrition : Clini-

cal Nutrition. Supplement 1, 39C, pp. 5–96.

Sturm, Roland. 2002. “The Effects of Obesity,

Smoking, and Drinking on Medical Problems

and Costs.” Health Affairs. March/April, 21, pp.245–54.

Testin, Robert F. 1995. “New Packaging Tech-

nologies.” Food and Drug Law Journal. 50:4, pp.

399 –413.

U.S. Department of Agriculture. 2000. “Major

Trends in the U.S. Food Supply, 1909 –1999.”

Food Review. 23:1. Available at http://www.ers.

usda.gov/publications/foodreview/jan2000/ .

U.S. Department of Commerce. 2000. U.S.

Census 2000 . Available at www.census.gov .

U.S. Department of Health and Human Ser-

vices. 1999. Mental Health: A Report of the Surgeon General . Rockville, Md.: U.S. Department of

Health and Human Services, Substance Abuse

and Mental Health Services Administration,

Center for Mental Health Services, National In-

stitutes of Health, National Institute of Mental

Health.


vices, National Center for Health Statistics.

1977. National Health Examination Survey I: 1959 –

1962 . Washington, D.C.: U.S. Department of

Health and Human Services, National Center

for Health Statistics, producer; Ann Arbor: Inter-University Consortium for Political and Social

Research, distributor.






1978. National Health and Nutrition Examination

Survey I . Washington, D.C.: U.S. Department of Health and Human Services, National Centerfor Health Statistics, producer; Ann Arbor: Inter-University Consortium for Political and SocialResearch, distributor.



1996. National Health and Nutrition Examination

Survey IIII . Washington, D.C.: U.S. Department of Health and Human Services, National Centerfor Health Statistics, producer; Ann Arbor: Inter-

University Consortium for Political and Social

Research, distributor.

Whitney, Eleanor N. and Corinne B. Cataldo.

1983. Understanding Normal and Clinical Nutrition .Belmont, Calif.: West-Wadsworth Publishing

Company.

World Health Organization. 2000. “Obesity:

Preventing and Managing the Global Epidemic.”

WHO Technical Report No. 894.

Young, Lisa R. and Marion Nestle. 2002. “The

Contribution of Expanding Portion Sizes to the

U.S. Obesity Epidemic.” American Journal of Pub-

lic Health. 92:2, pp. 246 –49.


jurnal metabolik

Documents